What is Tray.ai?
Tray.ai is an AI integration platform that connects, automates, and orchestrates data and AI across enterprise systems. It covers AI agent building, MCP governance, process automation, data integration, API management, and AI development in a single platform. The core output is connected, automated workflows that move data between apps and put AI agents to work in real business processes. Teams dealing with disconnected SaaS stacks, manual handoffs, or ungoverned AI deployments can use Tray.ai to wire everything together with enterprise controls built in. Access comes through a visual low-code builder or directly inside AI IDEs like Claude Code through a dedicated headless interface. Over 700 app connectors are available across the platform.
Tray.ai Video
Features & Benefits
- AI Agent Builder: build no-code production AI agents from knowledge sources, tools, and reasoning. Supports multi-agent orchestration where a master agent coordinates sub-agents per department. Each agent can take real actions in connected systems. This AI integration platform capability ships agents across Slack, Teams, chatbot surfaces, or any API endpoint from one build.
- MCP Gateway: spin up, version, publish, and deprecate managed MCP servers on a governed path. Any connector becomes a managed MCP tool with the right auth and governed scope. Composite tools package multi-step workflows into a single MCP call, cutting token consumption by an order of magnitude.
- Process Automation: build visual workflows with branching, approvals, and error handling. Ships any business process in days without custom code.
- Data Integration: run real-time syncs, change data capture, and batch pipelines across SaaS apps and data warehouses.
- API Management: publish, version, rate-limit, and govern APIs without custom middleware. Produces a full API portal on top of internal systems.
- Intelligent Document Processing: extract structured data from invoices, contracts, and forms using AI. Validates, routes, and pushes data directly into connected systems.
- Embedded Integrations: give end customers native integrations inside a product. Covers OAuth handling and white-label UI across 700+ connectors.
- Visual Low-Code Builder (Tray Build): drag-and-drop canvas for building workflows and agents. Supports branching, loops, and error paths.
- Headless Builder (Tray Headless): access the full platform from Claude Code, Cursor, Codex, or any MCP-compatible AI IDE. Includes config, typed tools, validation hooks, and six built-in skills for Claude Code.
- AI Steps: drop LLM calls, classification, and document extraction directly into any workflow step. Routes and governs LLM calls across the stack via an AI gateway. Natively connects to multiple major LLM providers. Vector tables store and query embeddings without a separate vector database.
- Smart Data Sources: ingest, partition, chunk, and embed data automatically. Keeps agent context current without manual updates.
- Connector Library: 700+ maintained connectors across major SaaS, database, and cloud services. One shared library used across agents, workflows, and MCP tools.
- Connector Generation (Claude CDK): generate production-grade connectors from an OpenAPI spec. Ships new integrations in minutes.
- Access Control and RBAC: one access model across workflows, agents, MCP tools, and data pipelines. Per-workspace, per-connector, per-MCP-tool granularity. Dynamic auth for MCP handles identity at the tool level.
- Full Platform Observability: every agent action, MCP tool call, and workflow run logged with who triggered it and what the outcome was. Token costs, latencies, and errors in one view.
- Audit Logging and Log Streaming: every action traceable to a real user. Streams in real time to SIEM tools. Configurable retention policies.
- Data Masking: built-in masking protects PII, PHI, and financial data as it moves through workflows. No custom logic required per step.
- Workflow Data Visibility: shows exactly where data flows inside a workflow, which systems it touches, and how it is transformed.
- Data Residency and Encryption: regional hosting in US, EU, and APAC. Data encrypted at rest and in transit. Dev, staging, and prod separated by approval gates.
- Serverless Elastic Execution: no infrastructure to provision. Governance, audit trails, and access controls scale automatically.
What can Tray.ai do?
- Connect SaaS apps without custom code
- Build AI agents that take actions in business systems
- Automate lead routing and enrichment
- Sync data between CRM and data warehouse in real time
- Govern MCP tool calls across the enterprise
- Automate employee onboarding across multiple systems
- Extract structured data from invoices and contracts
- Build and publish internal APIs
- Run multi-agent workflows across departments
- Automate IT helpdesk ticket resolution
- Process and route documents with AI
- Give customers native integrations inside a product
- Build workflows in Claude Code or Cursor
Real-World Applications
IT and operations teams managing large SaaS environments may find Tray.ai useful for automating provisioning, access management, and ticket resolution. An IT team can deploy an AI integration platform agent that resets passwords, provisions software, and closes tickets without a human touching each request. The same governance layer traces every action to a specific user, which makes audits manageable at scale.
Marketing and revenue operations teams can use Tray.ai’s AI integration platform capabilities to handle lead routing, data enrichment, and GTM pipeline quality. Inbound leads can be scored, deduped, and routed to the right rep in minutes. Data from multiple marketing and CRM systems can merge into a single enriched record that sales and analytics tools pull from directly.
Finance and procurement teams working across multiple systems can automate quote-to-cash workflows, invoice extraction, and approval routing. Intelligent document processing pulls structured data from contracts and invoices. AI integration platform workflows can then validate and push that data into billing or ERP systems without manual entry.
Development teams building SaaS products can ship native integrations to customers without writing custom connectors for every app. A team can publish new integrations across hundreds of apps in weeks rather than quarters. The same platform handles OAuth, rate limiting, and white-label UI, so the engineering focus stays on the product rather than integration scaffolding.
